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Homogenisation of Temperature and Precipitation Time Series with ACMANT3: Method Description and Efficiency Tests

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Homogenisation of Temperature and Precipitation Time Series with ACMANT3: Method Description and Efficiency Tests View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by MURAL - Maynooth University Research Archive Library INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 37: 1910–1921 (2017) Published online 19 July 2016 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.4822 Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests P. Domonkosa* andJ.Collb a Centre for Climate Change, University of Rovira i Virgili, Tortosa, Spain b Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Co Kildare, Ireland ABSTRACT: The development of Adapted Caussinus–Mestre Algorithm for homogenising Networks of Temperature series (ACMANT), one of the most successful homogenisation methods tested by the European project COST ES0601 (HOME) has been continued. The third generation of the software package ‘ACMANT3’ contains six programmes for homogenising temperature values or precipitation totals. These incorporate two models of the annual cycle of temperature biases and homogenisation either on a monthly or daily time scale. All ACMANT3 programmes are fully automatic and the method is therefore suitable for homogenising large datasets. This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method. The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and ANOVA application on the daily time scale. Examples of efficiency testsof monthly temperature homogenisation using artificially developed but realistic test datasets are presented. ACMANT3 canbe characterized by improved efficiency in comparison with earlier ACMANT versions, high missing data tolerance and improved user friendliness. Discussion concerning when the use of an automatic homogenisation method is recommended is included, and some caveats in relation to how and when ACMANT3 should be applied are provided. KEY WORDS time series; homogenisation; ACMANT; spatial interpolation; surface air temperature; precipitation Received 12 February 2016; Revised 31 May 2016; Accepted 8 June 2016 1. Introduction observational and data transfer errors. Certain data qual- ity problems can be eliminated by general quality con- For the analysis of climate change and climate variabil- trol (Durre et al., 2010; Menne et al., 2012), or with the ity the accuracy of observational data is of key importance analysis of the documents of the history of observations (Williams et al., 2012; Acquaotta and Fratianni, 2014). (so-called metadata, Bergstrom and Moberg, 2002; Pro- Although national meteorological services led by World hom et al., 2016). However, the statistical homogenisa- Meteorological Organisation (WMO) initiatives foster the tion of data provides additional quality control and allows production of high quality and comparable climatic data for improved temporal and spatial comparisons between temporally and spatially, technical changes in the mea- data for scientific purposes (Peterson et al., 1998; Beaulieu surement setup or observational practices often influence et al., 2008; Venema et al., 2012). When observational net- the usability of climate data records. Observational data works are sufficiently dense, relative homogenisation (i.e. can only be considered temporally homogeneous (here- homogenisation methods including spatial comparisons after: homogeneous) if temporal variations are exclusively of time series) can help to remove even relatively small influenced by weather and climate. In practice, several non-climatic biases from the data. Therefore, the statistical factors corrupt the homogeneity of climatic time series, methodology underpinning time series homogenisation is these include: station relocations, changes of instrumen- a widely studied topic of climatology (e.g. Series of Data tation, instrument position, site changes around the instru- Quality Control and Time Series Homogenisation, World ment, changes of the timing of reading instruments, etc. Meteorological Organisation, 1996–2014). (Aguilar et al., 2003; Menne et al., 2009; Acquaotta et al., Both the most common and frequent form of inhomo- 2016). A general observation is that long observational cli- geneity in a climate time series is the sudden shift of the matic records are seldom homogeneous, and that the qual- means, commonly referred to as a break or change-point. ity of climatic records may also be affected by occasional A set of breaks can be searched and corrected one-by-one in hierarchic structures (e.g. Alexandersson and Moberg, * Correspondence to: P. Domonkos, Centre for Climate Change, Uni- 1997), or jointly with appropriate mathematical tools. versity of Rovira i Virgili, Av. Remolins 13-15, 43500 Tortosa, Spain. When time series include multiple breaks, joint treatments E-mail: [email protected] have theoretical advantages over hierarchic techniques © 2016 Royal Meteorological Society HOMOGENISATION OF TIME SERIES WITH ACMANT3 1911 (Szentimrey et al., 2014; Lindau and Venema, 2016), as Caussinus–Mestre Algorithm for homogenising Networks in hierarchic techniques early phase errors are delivered to of Temperature series, Domonkos, 2011b) and the other the later steps of the homogenisation process. For purposes is Homogenization software in R (HOMER, Mestre et al., of this study ‘multiple break method’ means a method 2013), the interactive homogenisation method officially with joint detection of inhomogeneities, and one which recommended by HOME. Both HOMER and ACMANT incorporates the joint calculation of correction terms provide additional functionality relative to the parent for adjusting inhomogeneities. As observed temperature method PRODIGE. Therefore recently, HOMER and time series include five to seven breaks per 100 years on ACMANT have been applied more frequently than average (Menne et al., 2009; Venema et al., 2012), or even PRODIGE. more due to hidden short-term biases (Domonkos, 2011a; After the termination of HOME, the development of Rienzner and Gandolfi, 2011) multiple break methods ACMANT has continued, and the second generation of are of key importance in providing high level solutions ACMANT (ACMANT2) already incorporated methods for homogenisation tasks particularly in relation to the for the precipitation homogenisation and for the treat- homogenisation of temperature. Efficiency tests prove ment of daily data through downscaling the monthly that multiple break methods generally outperform other homogenisation results to daily scale (Domonkos, 2014, homogenisation methods (Domonkos, 2011a,; Domonkos 2015a). Most recently the development of ACMANT3 has et al., 2011; Venema et al., 2012). Although some inho- improved the efficiency and user friendliness further, and mogeneities result in gradually increasing biases instead some of these improvements are detailed below. of abrupt shifts of the means (e.g. urbanisation), this effect ACMANT3 is a complex software package incorporat- has little impact on the rank order of method efficiencies ing six programmes, these are: temperature homogeni- (Domonkos, 2011a). sation with a sinusoid annual cycle of biases; temper- The organisation of this paper is as follows: The devel- ature homogenisation with an irregular annual cycle of opment of multiple break methods and particularly the biases; precipitation homogenisation. Each of the pre- development of Adapted Caussinus–Mestre Algorithm for ceding three has monthly and daily homogenisation ver- homogenising Networks of Temperature (ACMANT) is sions (http://www.c3.urv.cat/data.html); and in total the described in the next section; in Section 3, the novel fea- six programmes incorporate 174 sub-routines. The soft- tures of ACMANT3 compared with earlier ACMANT ver- ware package also includes auxiliary files to support net- sions are presented; some efficiency results are presented work construction. However, despite its complicated struc- in Section 4; and the paper ends with a discussion and some ture, ACMANT provides the fastest method implemen- recommendations in Section 5. tation among all the available automatic homogenisation methods. As both HOMER and ACMANT have been developed 2. Development of multiple break methods and from PRODIGE, the two new multiple break methods ACMANT have several similarities. Table 1 summarizes the main similarities and differences of the two methods. Note that Although statistical break detections and corrections although HOMER is only for monthly homogenisation, the have been studied and applied for at least 90 years joint use of HOMER and Spline Daily Homogenization (Conrad, 1925), the theory and development of multi- (SPLIDHOM, Mestre et al., 2011) can be applied for daily ple break homogenisation appeared only in the 1990s data homogenisation (www.homogenisation.org), and in coincident with the more widespread use of personal Table 1 it is considered the daily homogenisation version computers. At that time two approaches to multiple of HOMER. break homogenisation were established, namely Mul- tiple Analysis of Series for Homogenisation (MASH), Szentimrey,
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